Implementation details: For training, we used the Adam optimizer [1]} (\(\epsilon = 10^{-3}\) ), batch size = 8, pretraining epochs (\(N_p\) ) = 100 and trained with TE for 100 epochs with a patience value of 25 epochs for early stopping (converged at \(\sim \) 90 epochs). For focal loss, we used \(\alpha =\)  0.25; \(\gamma =\)  2, and for contrastive loss, we used \(\tau =\)  0.5. The hyperparameters were chosen empirically. For \(F_{Seg}\) , we augmented data using translation (offset \(\in \)  [-50, 50] voxels), rotation (\(\theta \in \)  [-20o, 20o]), horizontal/vertical flipping and scaling (\(s \in \)  [0.9, 1.2]). The model was implemented using PyTorch 1.10.0 on Nvidia GeForce RTX 3090, taking \(\sim \) 10 mins/epoch for \(\sim \) 22,000 samples (training:validation = 90:10).
